Maximum Power Point Tracking using Fuzzy Logic based controllers compared to P&O technique in photovoltaic generator MANARI Otmane
ZAZI Malika
CHTOUKI Ihssane
ERCSLNL / LM2PI Mohammed V University RABAT
[email protected]
Electrical Engineering Department ENSET Rabat Mohammed V University RABAT
[email protected]
ERCSLNL / LM2PI Mohammed V University RABAT
[email protected]
ABSTRACT The use of renewable energies has grown significantly in the world. Given the growing demand for electrical energy. In this paper we discuss the optimization of the production of a GPV by two different techniques MPPT. In order to make a full operation of photovoltaic array, its compulsory to inaugurate a protocol or an algorithm to help extract, track and maximize the available power, this technique is called Maximum Power Point Tracking (MPPT).A several number of technique has been presented, such as the classic techniques; Perturb and Observe (P&O) [1], and incremental conductance [2], which are largely used mostly due to their simplicity of implementation and less complexity. In this paper, a Fuzzy Logic Controller (FLC) based MPPT technique [3] is compared to conventional P&O technique. The simulation in MATLAB/Simulink environment reveals the superior performance of FLC based technique over P&O method.
Keywords: PV power system, Fuzzy logic, P&O, MPPT. 1. INTRODUCTION Demand for electrical power has been steadily increasing in recent years, and production constraints, global warming, are driving research towards the development of renewable energy sources. In this context, photovoltaic (PV) systems offer a highly competitive solution. Many countries have recently embarked on development programs for photovoltaic generators that they have a several advantages: PV panels provide clean energy, Low maintenance costs , Very silent. Hence, they have some drawbacks: Requirement of additional equipment to convert direct Current (DC) to alternating current (AC) in order to be connected to the grid, Low efficiency levels (between 14%-25%). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. To copy otherwise, distribute, republish, or post, requires prior specific permission and/or a fee. Request permissions from
[email protected]. ICSDE '17, July 21 23, 2017, Rabat, Morocco © 2017 Association for Computing Machinery. ACM ISBN 978-1-4503-5281https://doi.org/10.1145/3128128.3128149
To overcome the problem of solar panel efficiency and achieve its maximum, it is necessary to optimize the design of all parts of the PV system. In addition, it is necessary to optimize the DC / DC converters ; used as an interface between the PV generator and the load, in order to extract the maximum power and to operate the GPV generator at its Maximum power point tracking (MPPT) under varying load and atmospheric conditions (irradiance and temperature). A significant number of MPPT control techniques have been developed since the 1970s, starting with simple techniques such as MPPT controllers based on voltage and current feedback [4], more efficient controllers using algorithms for Calculate MPP of the PV generator, the most used technique is Perturb and Observe (P & O) [1].However, in recent years, more robust control techniques have been associated with the MPPT control such as fuzzy logic[3], in order to increase the efficiency of solar panels. This paper is organized as follows: After a brief introduction, we present in section 2, the modeling of the photovoltaic generator and the DC/DC converter (Boost). Section 3, presents the description of the algorithms developed for the two techniques of tracking the PPM, namely the "P & O" and "Fuzzy Logic" controllers. Then in Section 4, we present and interpret the results of the simulations concerning the behavior of the PV system under the effect the two controllers "P & O" or "Fuzzy Logic", we present also the evaluation of the performance of each of the MPPT commands studied. Finally, in section 5, we conclude our contribution with a conclusion on our work.
2. MODELING of PHOTOVOLTAIC GENERATOR and the DC/DC CONVERTER 2.1. Modeling of Photovoltaic Generator The photovoltaic cell is modeled as a P-N semiconductor junction that directly transforms light energy into electricity [5]. The equivalent circuit shown in figure 1:[11]
2.1.2. The Influence of Solar Irradiance on PV Panel: A network of characteristics I = f (U) following with variable illumination (for a junction temperature of 25 ° C). It is noted that the voltage Vmax corresponding to the maximum power varies only very slightly as a function of the illumination, contrary to the current Imax, which increases strongly with the illumination.
Figure 1: Photovoltaic cell equivalent circuit The GPV photovoltaic generator is modeled by the Equation
The current of the solar panel I is a function of the photocurrent generated by the solar irradiation , where is the saturation current given by the manufacturer, ns and np are respectively the number of cells in series and in parallel, with an ideal factor of the PN junction, K is the Boltzmann coefficient = J / K, T is the temperature; the voltage at the terminals of the solar panel is V. The power delivered by the panel P is expressed by the equation:
Figure 3: I-V characteristic of a typical PV module for varied solar Irradiances.
P=V.I The operation of the generator depends strongly on the characteristics of the load with which it is connected. In addition, for different values of R, the optimal adaptation occurs for a single operating point) named maximum power point MPP. Consequently, in order for the generator to operate most often at its maximum point, the commonly used solution is to introduce a DC / DC converter, which acts as a load source adapter, in this case the generator, delivers maximum power.
2.1.1. Current / Voltage Characteristic The characteristic V I is a non-linear characteristic having a single optimal point where the power is maximum, 'MPP'[6]. The corresponding optimum voltage and current are MPPI and MPPV (Figure 2).
Figure 4: P-I characteristics of a typical PV module for varied solar Irradiances.
2.2. Modeling of DC/DC Converter The role of the DC-DC converter is to make the adaptation between the GPV and a DC load to have a maximum power transfer. The operating point is maintained near the MPP whatever the operating conditions (sunshine, temperature, load variation, etc.). The DC / DC static converter used is most frequently used as Boost converter (Figure 5) [7].
Figure 2: I-V and P-V characteristics of a typical PV module
value slows down the search for the MPP, so a compromise must be found between accuracy and speed. What makes this command quite difficult to optimize. The flowchart is represented in Figure 6. [12]
Figure 5: Boost converter equivalent circuit The operating principle of a boost converter is divided into two distinct phases depending on the state of the switch S: When the switch S is closed (on state), the current in the inductance will increase, and an energy in the form of magnetic energy is stored. The diode is therefore blocked, and the load will be disconnected. When the switch is open, the inductor is then in series with the generator and its voltage will be added to that of the generator: it is the boosting effect. The cumulative energy in the inductance will therefore be transferred to the capacitance. This converter is described by the following equation:
Where D is the duty cycle of the Boost converter.
3. MAXIMUM POWER POINT TRACKING The MPPT command, 'Maximum Power Point Tracking', is an essential command for optimal operation of the photovoltaic system. The principle of this control is based on the automatic variation of the duty cycle by bringing it to the optimum value to maximize the power delivered by the PV panel. For this reason, we will present and study the most popular control algorithms.
3.1. Perturb and Observe Algorithm The principle of this algorithm is to disrupt the voltage of the PV panel while acting on the duty cycle. In fact, following this Perturbation, the power provided by the PV panel is calculated at the instant (k), then it is compared with the previous one of the instant (k-1).If the power increases, it approaches the maximum power point, 'MPP' and the variation of the duty cycle is maintained in the same direction. On the contrary, if the power decreases, we move away from the point of maximum power, 'MPP'. Then, we must reverse the direction of the variation of the duty cycle. However, this technique presents some problems related to the oscillations around the MPP that it generates under steady conditions, because the search procedure of the PPM must be repeated periodically, forcing the system to oscillate permanently around the MPP. These oscillations can be minimized by reducing the value of the perturbation variable. However, a small increment
Figure 6: Flowchart of the "P & O" control algorithm
3.2. The MPPT Command Based on Fuzzy Logic. Commands based on fuzzy logic are increasingly used following the evolution of microcontrollers. In our case, the principle is based on two input variables, which are the error e and the change of error variation of the duty cycle used to control the static converter to search for the MPP. evolution of the parameters of entry. This approach is based on two essential concepts: the decomposition of a range of variation of a variable in the form of linguistic nuances: "big", "zero", "small" ... and the rules expressed in linguistic form. The system commands should evolve according to the observed variables: "If the error is positively Big and the variation of the error is positively Small, the variation of the output is Negatively Big ". These concepts are based on part of the fuzzy sub-set theory introduced by Zadeh [8]. A fuzzy regulator can be presented in different ways, but in general the presented presentation is split into three parts: fuzzification, which allows to pass from real variables to fuzzy variables, the heart of the regulator represented by the rules linking the inputs and finally the inference and defuzzification which make it possible, from the input fuzzy sets, to determine the actual output value, (figure 7). Figure 7 shows the configuration of the fuzzy controller which consists of: Inputs-Scaling, fuzzification, decision making, defuzzification, and the output.
3.2.2. Inference method: Inference is a step of defining a logical relationship between inputs and outputs. Indeed, rules of belonging will be defined for the output as it was done for the inputs, thanks to these rules an array of inference can be drawn (Table 1) [9]. It is obvious that a good knowledge of the system is required for the development of such a regulator. Indeed, as a rule, an input value is defined by two fuzzy functions with different degrees, so the output will also be defined by several functions, the question being to know with which degrees of membership. Several methods can answer this question. On our part, we used the Figure 7: Block diagram of Fuzzy Logic Controller
MAX-MIN method[10]. Table 1. Table of fuzzy rules
3.2.1. Fuzziffication:
E(k)
The goal of fuzzification is to transform the input variables into linguistic variables or fuzzy variables. In our case, we have two input variables that are the error E and the error variation CE at the instant k which are defined as follows:
Fuzzy Rules
Ce(k)
Thus, its variables will be qualified as Negative Big (NB), Negative Small (NS), Null Error or zero (ZE), Positive Small (PS) and Positive Big (PB)
NB
NS
ZE
PS
PB
NB
ZE
PB
PS
ZE
NB
NS
PB
PS
ZE
ZE
NB
ZE
PB
PS
ZE
NS
NB
PS
PB
ZE
ZE
NS
NB
PB
PB
ZE
NS
NB
ZE
3.2.3. Déffuzification: For a sampled data representation, the center of gravity is calculated by:
= The output values are de-fuzzified and multiplied by the scale factor to construct the current control signal. Its role is to regulate the fuzzy controller to obtain the continuation of the MPP.
Figure 8: Memb
4. RESULTS AND DISCUSSION In order to show the efficiency of the proposed system, simulations have been carried out in this part with a resistive load of 5 For this the value of the sunshine is changing to show the variation et in the atmospheric conditions all the day , so the simulation has been carried out with E = 1000 W / m²,900 W / m² and 800 W / m², and by Keeping the temperature constant at 25 ° C Table 2: parameter of PV PANEL and boost converter
Figure 9: Memb
Parameter Typical peak power Voltage et peak power
Figure 10:
D
Value ( (
Current at peak power
(
Short-circuit current
(
)
250 W
)
34.2 V
)
7A
)
8.1 A
Open-circuit Voltage
(
)
37.95V
Switching Frequency of the Boost Converter
100KHz
inductor L in the boost converter circuit
0.001H
Capacitor C value in the boost converter circuit
10-3 F
Figure 15: Power curve generated by P&O algorithm
Figure 11: Current curve generated by P&O algorithm Figure 16: Power curve generated by Fuzzy Logic algorithm
Figure 12: Current curve generated by Fuzzy Logic algorithm
Figure 13: Voltage curve generated by P&O algorithm
Figure 17: A comparison between current curve generated by P&O and Fuzzy Logic algorithm
Figure 18: A comparison between voltage curve generated by P&Oand Fuzzy Logic algorithm
Figure 14: Voltage curve generated by Fuzzy Logic algorithm Figure 19: A the comparison between power curve generated by P&O and Fuzzy Logic algorithm
The P & O algorithm is simple. In general, this algorithm strongly depends on the initial conditions and it has oscillations around the optimal value. The algorithm based on fuzzy logic is a robust and efficient algorithm. Indeed, this algorithm works at the optimal point without oscillations. In addition, it is characterized by good behavior in a transient state. However, the implementation of this type of algorithm is more complex than the classical algorithms. Moreover, the efficiency of this algorithm depends very much on the inference table. In the presence of the aforementioned, we deduce the remarks and interpretations below, concerning the behavior of the PV system with "P & O" and "Fuzzy" MPPT controllers: In either cases, the converter time or response time of the "Fuzzy" controller is faster than that of the "P & O" controller. The "P & O" controller has oscillations around the PPM, while the "Fuzzy" controller remains stable.
5. CONCLUSION In order to improve the efficiency of photovoltaic systems, various control algorithms have been studied to track the maximum power point .In this work, we began by presenting the design and simulation of a controller based on one of the popular technique, namely Perturb and Observe. Then we compared to an intelligent technique based on the fuzzy logic controllers. The results obtained with a fuzzy controller are better than those obtained with perturb and observe algorithm. Thus, control by fuzzy logic can be seen as a step towards a rapprochement between precise mathematical control and decision-making. In addition, these results confirm the correct operation of the controller (P and O) but show a better functioning of the fuzzy controller. The latter has proved that it has better performance, fast response time and very low permanent error, and is robust to the different variations of atmospheric conditions.
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